The University of Southampton
University of Southampton Institutional Repository

Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design

Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design
Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design
The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design for efficient and robust sparse kernel data modelling. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.
0 95338904 9
112-117
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Chen, S.
ac405529-3375-471a-8257-bda5c0d10e53
Hong, X.
b8f251c3-e142-4555-a54c-c504de966b03
Harris, C.J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Chen, S., Hong, X. and Harris, C.J. (2002) Sparse data modelling using combined locally regularized orthogonal least squares and D-optimality design. Combined Annual Conf. Institute of Automation, the Chinese Academy of Sciences, and Annual Conf. Chinese Automation and Computer Society in U.K., Beijing, China. 19 - 20 Sep 2002. pp. 112-117 .

Record type: Conference or Workshop Item (Other)

Abstract

The paper proposes to combine a locally regularized orthogonal least squares (LROLS) model selection with a D-optimality experimental design for efficient and robust sparse kernel data modelling. The LROLS algorithm alone is capable of producing a very parsimonious model with excellent generalization performance. The D-optimality design criterion further enhances the model efficiency and robustness. An added advantage is that the user only needs to specify a weighting for the D-optimality cost in the combined model selecting criterion and the entire model construction procedure becomes automatic. The value of this weighting does not influence the model selection procedure critically and it can be chosen with ease from a wide range of values.

Full text not available from this repository.

More information

Published date: September 2002
Additional Information: Presented at Combined Annual Conf. Institute of Automation, the Chinese Academy of Sciences, and Annual Conf. Chinese Automation and Computer Society in U.K.} (Beijing, China), Sept.20-21, 2002 Event Dates: Sept.20-21, 2002 Organisation: Chinese Automation and Computer Science Society in U.K. and the Institute of Automation of the Chinese Academy of Sciences
Venue - Dates: Combined Annual Conf. Institute of Automation, the Chinese Academy of Sciences, and Annual Conf. Chinese Automation and Computer Society in U.K., Beijing, China, 2002-09-19 - 2002-09-20
Organisations: Southampton Wireless Group

Identifiers

Local EPrints ID: 256821
URI: http://eprints.soton.ac.uk/id/eprint/256821
ISBN: 0 95338904 9
PURE UUID: c7f1d79c-b1b9-451b-8dc6-2caf4be36d03

Catalogue record

Date deposited: 07 Oct 2002
Last modified: 29 Jan 2020 15:26

Export record

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×